The invention relates generally to automated diagnostic systems and methods and more particularly to a method and system for diagnosing faults in a particular device within a fleet of devices.
Diagnostic systems are generally developed based on analyzing data characteristics in fleets of equipment having similar behavioral patterns. Diagnostic systems may also use expert knowledge in the form of examples or validation cases in order to make accurate diagnoses. Validation cases typically include historical data of equipment parameter trend patterns captured and extracted by expert surveys and data mining techniques.
Diagnostic systems developed based on analyzing the data characteristics exhibited by fleets of equipment usually tend to provide accurate diagnosis for equipment whose behavioral pattern is close to the average behavioral pattern exhibited by the fleet. However, when individual equipment data characteristics vary substantially from the data characteristics exhibited by the fleet, “fleet-based diagnostic models” may provide inaccurate diagnosis, leading to the generation of false positives or false negatives. Further, “fleet-based diagnostic models” may fail to provide accurate diagnostic results when individual units within the same fleet have different signal to noise levels. In particular, for equipment with low noise levels, a “fleet-based diagnostic model” may fail to detect a fault that only causes subtle shifts below a threshold level, resulting in missed detections or false negatives. On the other hand, for equipment that normally operates above the typical noise level, a noisy signal may cause relatively large shifts that exceed rule thresholds, causing the “fleet-based diagnostic model” to generate a false diagnosis of a fault.
It would be desirable to develop a personalized diagnostic model based on individual engine data characteristics. In addition, it would be desirable to develop a personalized diagnostic model that automatically adapts to individual equipment data characteristics at various noise levels and improves model sensitivity and diagnostic accuracy.